Carga y limpieza preliminar de los datos
Los datos que se van a analizar en este documento, proceden de la compilación hecha por usuarios de Kaggle. La fecha del análisis empieza el 6 de Abril de 2020, utilizando la versión número 73 recopilada en la web anterior.
datos <- read.csv("covid_19_clean_complete.csv")
View(datos)
datos %>% head(10) %>% kable() %>% kable_styling()
|
Province.State
|
Country.Region
|
Lat
|
Long
|
Date
|
Confirmed
|
Deaths
|
Recovered
|
Active
|
WHO.Region
|
|
|
Afghanistan
|
33.93911
|
67.70995
|
2020-01-22
|
0
|
0
|
0
|
0
|
Eastern Mediterranean
|
|
|
Albania
|
41.15330
|
20.16830
|
2020-01-22
|
0
|
0
|
0
|
0
|
Europe
|
|
|
Algeria
|
28.03390
|
1.65960
|
2020-01-22
|
0
|
0
|
0
|
0
|
Africa
|
|
|
Andorra
|
42.50630
|
1.52180
|
2020-01-22
|
0
|
0
|
0
|
0
|
Europe
|
|
|
Angola
|
-11.20270
|
17.87390
|
2020-01-22
|
0
|
0
|
0
|
0
|
Africa
|
|
|
Antigua and Barbuda
|
17.06080
|
-61.79640
|
2020-01-22
|
0
|
0
|
0
|
0
|
Americas
|
|
|
Argentina
|
-38.41610
|
-63.61670
|
2020-01-22
|
0
|
0
|
0
|
0
|
Americas
|
|
|
Armenia
|
40.06910
|
45.03820
|
2020-01-22
|
0
|
0
|
0
|
0
|
Europe
|
|
Australian Capital Territory
|
Australia
|
-35.47350
|
149.01240
|
2020-01-22
|
0
|
0
|
0
|
0
|
Western Pacific
|
|
New South Wales
|
Australia
|
-33.86880
|
151.20930
|
2020-01-22
|
0
|
0
|
0
|
0
|
Western Pacific
|
Estructura de los datos
str(datos)
## 'data.frame': 49068 obs. of 10 variables:
## $ Province.State: chr "" "" "" "" ...
## $ Country.Region: chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Lat : num 33.9 41.2 28 42.5 -11.2 ...
## $ Long : num 67.71 20.17 1.66 1.52 17.87 ...
## $ Date : chr "2020-01-22" "2020-01-22" "2020-01-22" "2020-01-22" ...
## $ Confirmed : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Deaths : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Recovered : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Active : int 0 0 0 0 0 0 0 0 0 0 ...
## $ WHO.Region : chr "Eastern Mediterranean" "Europe" "Africa" "Europe" ...
colnames(datos) = c("Provincia_Estado",
"Pais_Region",
"Latitud", # N+ o S-
"Longitud", # E+ o W-
"Fecha",
"Casos_Confirmados",
"Casos_Muertos",
"Casos_Recuperados",
"Casos_Activos",
"WHO_Region"
)
datos %>% head() %>% kable() %>% kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
|
|
Afghanistan
|
33.93911
|
67.70995
|
2020-01-22
|
0
|
0
|
0
|
0
|
Eastern Mediterranean
|
|
|
Albania
|
41.15330
|
20.16830
|
2020-01-22
|
0
|
0
|
0
|
0
|
Europe
|
|
|
Algeria
|
28.03390
|
1.65960
|
2020-01-22
|
0
|
0
|
0
|
0
|
Africa
|
|
|
Andorra
|
42.50630
|
1.52180
|
2020-01-22
|
0
|
0
|
0
|
0
|
Europe
|
|
|
Angola
|
-11.20270
|
17.87390
|
2020-01-22
|
0
|
0
|
0
|
0
|
Africa
|
|
|
Antigua and Barbuda
|
17.06080
|
-61.79640
|
2020-01-22
|
0
|
0
|
0
|
0
|
Americas
|
- Cualitativas se convierten con
factor o bien as.factor.
- Ordinales se convierten con
ordered.
- Cuantitativos se convierten con
as.numeric.
datos$Provincia_Estado<- as.factor(datos$Provincia_Estado)
datos$Pais_Region<- as.factor(datos$Pais_Region)
datos$Fecha<-as.factor(datos$Fecha)
Cambia de Caracteres o Factor a Fecha
datos$Fecha %<>% as.Date(format="%Y-%m-%d")
str(datos)
## 'data.frame': 49068 obs. of 10 variables:
## $ Provincia_Estado : Factor w/ 79 levels "","Alberta","Anguilla",..: 1 1 1 1 1 1 1 1 6 47 ...
## $ Pais_Region : Factor w/ 187 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 9 ...
## $ Latitud : num 33.9 41.2 28 42.5 -11.2 ...
## $ Longitud : num 67.71 20.17 1.66 1.52 17.87 ...
## $ Fecha : Date, format: "2020-01-22" "2020-01-22" ...
## $ Casos_Confirmados: int 0 0 0 0 0 0 0 0 0 0 ...
## $ Casos_Muertos : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Casos_Recuperados: int 0 0 0 0 0 0 0 0 0 0 ...
## $ Casos_Activos : int 0 0 0 0 0 0 0 0 0 0 ...
## $ WHO_Region : chr "Eastern Mediterranean" "Europe" "Africa" "Europe" ...
Uso de lubridate
#datos$Fecha %<>% mdy()
datos %<>%
mutate(Casos_Enfermos = Casos_Confirmados - Casos_Muertos - Casos_Recuperados)
datos %>% filter(Casos_Confirmados > 100000) %>% head(10) %>% kable() %>% kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
Casos_Enfermos
|
|
|
US
|
40.00000
|
-100.00000
|
2020-03-27
|
102276
|
2300
|
869
|
99107
|
Americas
|
99107
|
|
|
US
|
40.00000
|
-100.00000
|
2020-03-28
|
122069
|
2934
|
1072
|
118063
|
Americas
|
118063
|
|
|
US
|
40.00000
|
-100.00000
|
2020-03-29
|
141205
|
3561
|
2665
|
134979
|
Americas
|
134979
|
|
|
Italy
|
41.87194
|
12.56738
|
2020-03-30
|
101739
|
11591
|
14620
|
75528
|
Europe
|
75528
|
|
|
US
|
40.00000
|
-100.00000
|
2020-03-30
|
162707
|
4381
|
5644
|
152682
|
Americas
|
152682
|
|
|
Italy
|
41.87194
|
12.56738
|
2020-03-31
|
105792
|
12428
|
15729
|
77635
|
Europe
|
77635
|
|
|
US
|
40.00000
|
-100.00000
|
2020-03-31
|
188724
|
5605
|
7024
|
176095
|
Americas
|
176095
|
|
|
Italy
|
41.87194
|
12.56738
|
2020-04-01
|
110574
|
13155
|
16847
|
80572
|
Europe
|
80572
|
|
|
Spain
|
40.46367
|
-3.74922
|
2020-04-01
|
104118
|
9387
|
22647
|
72084
|
Europe
|
72084
|
|
|
US
|
40.00000
|
-100.00000
|
2020-04-01
|
214205
|
6846
|
8474
|
198885
|
Americas
|
198885
|
datos %>% filter(Casos_Enfermos <0) %>% kable() %>% kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
Casos_Enfermos
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-24
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-25
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-26
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-27
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-28
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-29
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-30
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-03-31
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.195900
|
109.74530
|
2020-04-01
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Channel Islands
|
United Kingdom
|
49.372300
|
-2.36440
|
2020-05-23
|
558
|
45
|
515
|
-2
|
Europe
|
-2
|
|
Channel Islands
|
United Kingdom
|
49.372300
|
-2.36440
|
2020-05-24
|
558
|
45
|
517
|
-4
|
Europe
|
-4
|
|
Channel Islands
|
United Kingdom
|
49.372300
|
-2.36440
|
2020-05-25
|
559
|
45
|
517
|
-3
|
Europe
|
-3
|
|
Channel Islands
|
United Kingdom
|
49.372300
|
-2.36440
|
2020-05-30
|
560
|
45
|
525
|
-10
|
Europe
|
-10
|
|
Channel Islands
|
United Kingdom
|
49.372300
|
-2.36440
|
2020-05-31
|
560
|
45
|
528
|
-13
|
Europe
|
-13
|
|
Channel Islands
|
United Kingdom
|
49.372300
|
-2.36440
|
2020-06-01
|
560
|
45
|
528
|
-13
|
Europe
|
-13
|
|
Channel Islands
|
United Kingdom
|
49.372300
|
-2.36440
|
2020-06-02
|
560
|
46
|
528
|
-14
|
Europe
|
-14
|
|
|
Liechtenstein
|
47.140000
|
9.55000
|
2020-06-23
|
82
|
2
|
81
|
-1
|
Europe
|
-1
|
|
|
Uganda
|
1.373333
|
32.29028
|
2020-07-20
|
1069
|
0
|
1071
|
-2
|
Africa
|
-2
|
datos %>% filter(Provincia_Estado == "Hainan" ) %>% kable() %>% kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
Casos_Enfermos
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-22
|
4
|
0
|
0
|
4
|
Western Pacific
|
4
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-23
|
5
|
0
|
0
|
5
|
Western Pacific
|
5
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-24
|
8
|
0
|
0
|
8
|
Western Pacific
|
8
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-25
|
19
|
0
|
0
|
19
|
Western Pacific
|
19
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-26
|
22
|
0
|
0
|
22
|
Western Pacific
|
22
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-27
|
33
|
1
|
0
|
32
|
Western Pacific
|
32
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-28
|
40
|
1
|
0
|
39
|
Western Pacific
|
39
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-29
|
43
|
1
|
0
|
42
|
Western Pacific
|
42
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-30
|
46
|
1
|
1
|
44
|
Western Pacific
|
44
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-01-31
|
52
|
1
|
1
|
50
|
Western Pacific
|
50
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-01
|
62
|
1
|
1
|
60
|
Western Pacific
|
60
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-02
|
64
|
1
|
4
|
59
|
Western Pacific
|
59
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-03
|
72
|
1
|
4
|
67
|
Western Pacific
|
67
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-04
|
80
|
1
|
5
|
74
|
Western Pacific
|
74
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-05
|
99
|
1
|
5
|
93
|
Western Pacific
|
93
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-06
|
106
|
1
|
8
|
97
|
Western Pacific
|
97
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-07
|
117
|
2
|
10
|
105
|
Western Pacific
|
105
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-08
|
124
|
2
|
14
|
108
|
Western Pacific
|
108
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-09
|
131
|
3
|
19
|
109
|
Western Pacific
|
109
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-10
|
138
|
3
|
19
|
116
|
Western Pacific
|
116
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-11
|
144
|
3
|
20
|
121
|
Western Pacific
|
121
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-12
|
157
|
4
|
27
|
126
|
Western Pacific
|
126
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-13
|
157
|
4
|
30
|
123
|
Western Pacific
|
123
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-14
|
159
|
4
|
43
|
112
|
Western Pacific
|
112
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-15
|
162
|
4
|
39
|
119
|
Western Pacific
|
119
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-16
|
162
|
4
|
52
|
106
|
Western Pacific
|
106
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-17
|
163
|
4
|
59
|
100
|
Western Pacific
|
100
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-18
|
163
|
4
|
79
|
80
|
Western Pacific
|
80
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-19
|
168
|
4
|
84
|
80
|
Western Pacific
|
80
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-20
|
168
|
4
|
86
|
78
|
Western Pacific
|
78
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-21
|
168
|
4
|
95
|
69
|
Western Pacific
|
69
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-22
|
168
|
4
|
104
|
60
|
Western Pacific
|
60
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-23
|
168
|
5
|
106
|
57
|
Western Pacific
|
57
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-24
|
168
|
5
|
116
|
47
|
Western Pacific
|
47
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-25
|
168
|
5
|
124
|
39
|
Western Pacific
|
39
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-26
|
168
|
5
|
129
|
34
|
Western Pacific
|
34
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-27
|
168
|
5
|
131
|
32
|
Western Pacific
|
32
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-28
|
168
|
5
|
133
|
30
|
Western Pacific
|
30
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-02-29
|
168
|
5
|
148
|
15
|
Western Pacific
|
15
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-01
|
168
|
5
|
149
|
14
|
Western Pacific
|
14
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-02
|
168
|
5
|
151
|
12
|
Western Pacific
|
12
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-03
|
168
|
5
|
155
|
8
|
Western Pacific
|
8
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-04
|
168
|
5
|
158
|
5
|
Western Pacific
|
5
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-05
|
168
|
6
|
158
|
4
|
Western Pacific
|
4
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-06
|
168
|
6
|
158
|
4
|
Western Pacific
|
4
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-07
|
168
|
6
|
158
|
4
|
Western Pacific
|
4
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-08
|
168
|
6
|
159
|
3
|
Western Pacific
|
3
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-09
|
168
|
6
|
159
|
3
|
Western Pacific
|
3
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-10
|
168
|
6
|
159
|
3
|
Western Pacific
|
3
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-11
|
168
|
6
|
159
|
3
|
Western Pacific
|
3
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-12
|
168
|
6
|
160
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-13
|
168
|
6
|
160
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-14
|
168
|
6
|
160
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-15
|
168
|
6
|
160
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-16
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-17
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-18
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-19
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-20
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-21
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-22
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-23
|
168
|
6
|
161
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-24
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-25
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-26
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-27
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-28
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-29
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-30
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-03-31
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-01
|
168
|
6
|
168
|
-6
|
Western Pacific
|
-6
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-02
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-03
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-04
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-05
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-06
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-07
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-08
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-09
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-10
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-11
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-12
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-13
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-14
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-15
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-16
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-17
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-18
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-19
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-20
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-21
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-22
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-23
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-24
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-25
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-26
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-27
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-28
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-29
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-04-30
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-01
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-02
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-03
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-04
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-05
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-06
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-07
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-08
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-09
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-10
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-11
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-12
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-13
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-14
|
168
|
6
|
162
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-15
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-16
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-17
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-18
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-19
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-20
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-21
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-22
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-23
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-24
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-25
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-26
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-27
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-28
|
169
|
6
|
162
|
1
|
Western Pacific
|
1
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-29
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-30
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-05-31
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-01
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-02
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-03
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-04
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-05
|
169
|
6
|
163
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-06
|
170
|
6
|
162
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-07
|
170
|
6
|
162
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-08
|
170
|
6
|
162
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-09
|
170
|
6
|
162
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-10
|
170
|
6
|
162
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-11
|
170
|
6
|
162
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-12
|
171
|
6
|
162
|
3
|
Western Pacific
|
3
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-13
|
171
|
6
|
162
|
3
|
Western Pacific
|
3
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-14
|
171
|
6
|
162
|
3
|
Western Pacific
|
3
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-15
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-16
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-17
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-18
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-19
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-20
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-21
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-22
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-23
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-24
|
171
|
6
|
163
|
2
|
Western Pacific
|
2
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-25
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-26
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-27
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-28
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-29
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-06-30
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-01
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-02
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-03
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-04
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-05
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-06
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-07
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-08
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-09
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-10
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-11
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-12
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-13
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-14
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-15
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-16
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-17
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-18
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-19
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-20
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-21
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-22
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-23
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-24
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-25
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-26
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
|
Hainan
|
China
|
19.1959
|
109.7453
|
2020-07-27
|
171
|
6
|
165
|
0
|
Western Pacific
|
0
|
datos %>% filter(Pais_Region == "Chile") %>% kable() %>% kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
Casos_Enfermos
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-22
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-23
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-24
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-25
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-26
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-27
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-28
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-29
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-30
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-01-31
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-01
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-02
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-04
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-05
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-06
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-07
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-08
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-09
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-10
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-11
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-12
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-13
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-14
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-15
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-16
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-17
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-18
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-19
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-20
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-21
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-22
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-23
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-24
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-25
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-26
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-27
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-28
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-02-29
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-01
|
9
|
0
|
0
|
9
|
Americas
|
9
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-02
|
9
|
0
|
0
|
9
|
Americas
|
9
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-03
|
10
|
0
|
0
|
10
|
Americas
|
10
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-04
|
10
|
0
|
0
|
10
|
Americas
|
10
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-05
|
13
|
0
|
0
|
13
|
Americas
|
13
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-06
|
13
|
0
|
0
|
13
|
Americas
|
13
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-07
|
13
|
0
|
0
|
13
|
Americas
|
13
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-08
|
20
|
0
|
0
|
20
|
Americas
|
20
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-09
|
20
|
0
|
0
|
20
|
Americas
|
20
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-10
|
25
|
0
|
0
|
25
|
Americas
|
25
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-11
|
35
|
0
|
0
|
35
|
Americas
|
35
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-12
|
35
|
0
|
0
|
35
|
Americas
|
35
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-13
|
55
|
0
|
0
|
55
|
Americas
|
55
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-14
|
88
|
0
|
0
|
88
|
Americas
|
88
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-15
|
101
|
0
|
0
|
101
|
Americas
|
101
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-16
|
182
|
0
|
0
|
182
|
Americas
|
182
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-17
|
228
|
0
|
0
|
228
|
Americas
|
228
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-18
|
265
|
0
|
0
|
265
|
Americas
|
265
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-19
|
265
|
0
|
0
|
265
|
Americas
|
265
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-20
|
461
|
0
|
6
|
455
|
Americas
|
455
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-21
|
592
|
0
|
6
|
586
|
Americas
|
586
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-22
|
687
|
1
|
8
|
678
|
Americas
|
678
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-23
|
801
|
2
|
8
|
791
|
Americas
|
791
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-24
|
977
|
2
|
17
|
958
|
Americas
|
958
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-25
|
1197
|
3
|
22
|
1172
|
Americas
|
1172
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-26
|
1361
|
4
|
22
|
1335
|
Americas
|
1335
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-27
|
1665
|
5
|
43
|
1617
|
Americas
|
1617
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-28
|
2015
|
6
|
61
|
1948
|
Americas
|
1948
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-29
|
2245
|
7
|
75
|
2163
|
Americas
|
2163
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-30
|
2555
|
8
|
156
|
2391
|
Americas
|
2391
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-03-31
|
2844
|
12
|
156
|
2676
|
Americas
|
2676
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-01
|
3137
|
16
|
234
|
2887
|
Americas
|
2887
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-02
|
3510
|
18
|
335
|
3157
|
Americas
|
3157
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-03
|
3843
|
22
|
427
|
3394
|
Americas
|
3394
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-04
|
4355
|
27
|
528
|
3800
|
Americas
|
3800
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-05
|
4665
|
34
|
618
|
4013
|
Americas
|
4013
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-06
|
5009
|
37
|
728
|
4244
|
Americas
|
4244
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-07
|
5310
|
43
|
898
|
4369
|
Americas
|
4369
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-08
|
5740
|
48
|
1115
|
4577
|
Americas
|
4577
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-09
|
6166
|
57
|
1274
|
4835
|
Americas
|
4835
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-10
|
6695
|
65
|
1571
|
5059
|
Americas
|
5059
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-11
|
7366
|
73
|
1864
|
5429
|
Americas
|
5429
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-12
|
7652
|
80
|
2059
|
5513
|
Americas
|
5513
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-13
|
7964
|
82
|
2367
|
5515
|
Americas
|
5515
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-14
|
8356
|
92
|
2646
|
5618
|
Americas
|
5618
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-15
|
8712
|
94
|
2937
|
5681
|
Americas
|
5681
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-16
|
9246
|
105
|
3299
|
5842
|
Americas
|
5842
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-17
|
9691
|
116
|
3621
|
5954
|
Americas
|
5954
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-18
|
10598
|
126
|
4035
|
6437
|
Americas
|
6437
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-19
|
10956
|
133
|
4338
|
6485
|
Americas
|
6485
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-20
|
11375
|
139
|
4676
|
6560
|
Americas
|
6560
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-21
|
11700
|
147
|
4969
|
6584
|
Americas
|
6584
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-22
|
12164
|
160
|
5386
|
6618
|
Americas
|
6618
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-23
|
12680
|
168
|
5804
|
6708
|
Americas
|
6708
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-24
|
13174
|
174
|
6327
|
6673
|
Americas
|
6673
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-25
|
14537
|
181
|
6746
|
7610
|
Americas
|
7610
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-26
|
15010
|
189
|
7024
|
7797
|
Americas
|
7797
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-27
|
15492
|
198
|
7327
|
7967
|
Americas
|
7967
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-28
|
16044
|
207
|
7710
|
8127
|
Americas
|
8127
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-29
|
16564
|
216
|
8057
|
8291
|
Americas
|
8291
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-04-30
|
17702
|
227
|
8580
|
8895
|
Americas
|
8895
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-01
|
18687
|
234
|
9018
|
9435
|
Americas
|
9435
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-02
|
21213
|
247
|
9572
|
11394
|
Americas
|
11394
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-03
|
22441
|
260
|
10041
|
12140
|
Americas
|
12140
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-04
|
23421
|
270
|
10415
|
12736
|
Americas
|
12736
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-05
|
24794
|
275
|
10710
|
13809
|
Americas
|
13809
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-06
|
25826
|
281
|
11189
|
14356
|
Americas
|
14356
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-07
|
27359
|
285
|
11664
|
15410
|
Americas
|
15410
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-08
|
28750
|
294
|
12160
|
16296
|
Americas
|
16296
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-09
|
32208
|
304
|
12667
|
19237
|
Americas
|
19237
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-10
|
33855
|
312
|
13112
|
20431
|
Americas
|
20431
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-11
|
35052
|
323
|
13605
|
21124
|
Americas
|
21124
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-12
|
36710
|
335
|
14125
|
22250
|
Americas
|
22250
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-13
|
39370
|
346
|
14865
|
24159
|
Americas
|
24159
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-14
|
42029
|
368
|
15655
|
26006
|
Americas
|
26006
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-15
|
44531
|
394
|
16614
|
27523
|
Americas
|
27523
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-16
|
50016
|
421
|
18014
|
31581
|
Americas
|
31581
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-17
|
52369
|
450
|
19213
|
32706
|
Americas
|
32706
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-18
|
54647
|
478
|
20165
|
34004
|
Americas
|
34004
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-19
|
58167
|
509
|
21507
|
36151
|
Americas
|
36151
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-20
|
62205
|
544
|
22504
|
39157
|
Americas
|
39157
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-21
|
66169
|
589
|
23992
|
41588
|
Americas
|
41588
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-22
|
70445
|
630
|
25342
|
44473
|
Americas
|
44473
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-23
|
80287
|
673
|
26546
|
53068
|
Americas
|
53068
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-24
|
83996
|
718
|
28148
|
55130
|
Americas
|
55130
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-25
|
88891
|
761
|
29302
|
58828
|
Americas
|
58828
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-26
|
92855
|
806
|
30915
|
61134
|
Americas
|
61134
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-27
|
97183
|
841
|
33540
|
62802
|
Americas
|
62802
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-28
|
101837
|
890
|
36115
|
64832
|
Americas
|
64832
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-29
|
105532
|
944
|
38598
|
65990
|
Americas
|
65990
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-30
|
118720
|
997
|
40431
|
77292
|
Americas
|
77292
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-05-31
|
123550
|
1054
|
42727
|
79769
|
Americas
|
79769
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-01
|
129020
|
1113
|
44946
|
82961
|
Americas
|
82961
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-02
|
132548
|
1188
|
86173
|
45187
|
Americas
|
45187
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-03
|
137490
|
1275
|
90748
|
45467
|
Americas
|
45467
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-04
|
142154
|
1356
|
95631
|
45167
|
Americas
|
45167
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-05
|
146361
|
1448
|
99358
|
45555
|
Americas
|
45555
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-06
|
160351
|
1541
|
103817
|
54993
|
Americas
|
54993
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-07
|
166756
|
1637
|
108150
|
56969
|
Americas
|
56969
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-08
|
171452
|
2264
|
112248
|
56940
|
Americas
|
56940
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-09
|
175365
|
2283
|
117361
|
55721
|
Americas
|
55721
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-10
|
181062
|
2475
|
121780
|
56807
|
Americas
|
56807
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-11
|
186698
|
2648
|
126444
|
57606
|
Americas
|
57606
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-12
|
193452
|
2870
|
131358
|
59224
|
Americas
|
59224
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-13
|
201634
|
3101
|
137296
|
61237
|
Americas
|
61237
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-14
|
208572
|
3323
|
143704
|
61545
|
Americas
|
61545
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-15
|
213715
|
3362
|
148792
|
61561
|
Americas
|
61561
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-16
|
218728
|
3383
|
156232
|
59113
|
Americas
|
59113
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-17
|
220628
|
3615
|
181931
|
35082
|
Americas
|
35082
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-18
|
225103
|
3841
|
186441
|
34821
|
Americas
|
34821
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-19
|
231393
|
4093
|
191491
|
35809
|
Americas
|
35809
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-20
|
236748
|
4295
|
196609
|
35844
|
Americas
|
35844
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-21
|
242355
|
4479
|
200569
|
37307
|
Americas
|
37307
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-22
|
246963
|
4502
|
205397
|
37064
|
Americas
|
37064
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-23
|
250767
|
4505
|
210570
|
35692
|
Americas
|
35692
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-24
|
254416
|
4731
|
215093
|
34592
|
Americas
|
34592
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-25
|
259064
|
4903
|
219327
|
34834
|
Americas
|
34834
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-26
|
263360
|
5068
|
223431
|
34861
|
Americas
|
34861
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-27
|
267766
|
5347
|
228055
|
34364
|
Americas
|
34364
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-28
|
271982
|
5509
|
232210
|
34263
|
Americas
|
34263
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-29
|
275999
|
5575
|
236154
|
34270
|
Americas
|
34270
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-06-30
|
279393
|
5688
|
241229
|
32476
|
Americas
|
32476
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-01
|
282043
|
5753
|
245443
|
30847
|
Americas
|
30847
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-02
|
284541
|
5920
|
249247
|
29374
|
Americas
|
29374
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-03
|
288089
|
6051
|
253343
|
28695
|
Americas
|
28695
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-04
|
291847
|
6192
|
257451
|
28204
|
Americas
|
28204
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-05
|
295532
|
6308
|
261039
|
28185
|
Americas
|
28185
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-06
|
298557
|
6384
|
264378
|
27795
|
Americas
|
27795
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-07
|
301019
|
6434
|
268251
|
26334
|
Americas
|
26334
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-08
|
303083
|
6573
|
271741
|
24769
|
Americas
|
24769
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-09
|
306216
|
6682
|
274922
|
24612
|
Americas
|
24612
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-10
|
309274
|
6781
|
278053
|
24440
|
Americas
|
24440
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-11
|
312029
|
6881
|
281114
|
24034
|
Americas
|
24034
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-12
|
315041
|
6979
|
283902
|
24160
|
Americas
|
24160
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-13
|
317657
|
7024
|
286556
|
24077
|
Americas
|
24077
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-14
|
319493
|
7069
|
289220
|
23204
|
Americas
|
23204
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-15
|
321205
|
7186
|
292085
|
21934
|
Americas
|
21934
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-16
|
323698
|
7290
|
295301
|
21107
|
Americas
|
21107
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-17
|
326439
|
8347
|
296814
|
21278
|
Americas
|
21278
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-18
|
328846
|
8445
|
299449
|
20952
|
Americas
|
20952
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-19
|
330930
|
8503
|
301794
|
20633
|
Americas
|
20633
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-20
|
333029
|
8633
|
303992
|
20404
|
Americas
|
20404
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-21
|
334683
|
8677
|
306816
|
19190
|
Americas
|
19190
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-22
|
336402
|
8722
|
309241
|
18439
|
Americas
|
18439
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-23
|
338759
|
8838
|
311431
|
18490
|
Americas
|
18490
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-24
|
341304
|
8914
|
313696
|
18694
|
Americas
|
18694
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-25
|
343592
|
9020
|
316169
|
18403
|
Americas
|
18403
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-26
|
345790
|
9112
|
318095
|
18583
|
Americas
|
18583
|
|
|
Chile
|
-35.6751
|
-71.543
|
2020-07-27
|
347923
|
9187
|
319954
|
18782
|
Americas
|
18782
|
Analisis Geografico
#datos_europa<-datos[datos$Latitud > 38 & datos$Longitud > -25 & datos$Longitud < 30 , ]
#nrow(datos_europa)
datos_europa = datos %>%
filter(Latitud > 38, between(Longitud, -25, 30))
table (datos_europa$Pais_Region) %>%
as.data.frame() %>%
filter(Freq > 0) %>%
kable() %>%
kable_styling()
|
Var1
|
Freq
|
|
Albania
|
188
|
|
Andorra
|
188
|
|
Austria
|
188
|
|
Belarus
|
188
|
|
Belgium
|
188
|
|
Bosnia and Herzegovina
|
188
|
|
Bulgaria
|
188
|
|
Croatia
|
188
|
|
Czechia
|
188
|
|
Denmark
|
376
|
|
Estonia
|
188
|
|
Finland
|
188
|
|
France
|
188
|
|
Germany
|
188
|
|
Greece
|
188
|
|
Holy See
|
188
|
|
Hungary
|
188
|
|
Iceland
|
188
|
|
Ireland
|
188
|
|
Italy
|
188
|
|
Kosovo
|
188
|
|
Latvia
|
188
|
|
Liechtenstein
|
188
|
|
Lithuania
|
188
|
|
Luxembourg
|
188
|
|
Moldova
|
188
|
|
Monaco
|
188
|
|
Montenegro
|
188
|
|
Netherlands
|
188
|
|
North Macedonia
|
188
|
|
Norway
|
188
|
|
Poland
|
188
|
|
Portugal
|
188
|
|
Romania
|
188
|
|
San Marino
|
188
|
|
Serbia
|
188
|
|
Slovakia
|
188
|
|
Slovenia
|
188
|
|
Spain
|
188
|
|
Sweden
|
188
|
|
Switzerland
|
188
|
|
United Kingdom
|
564
|
datos_europa %>%
filter(Fecha == ymd("2020-03-15")) %>%
kable() %>%
kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
Casos_Enfermos
|
|
|
Albania
|
41.15330
|
20.168300
|
2020-03-15
|
42
|
1
|
0
|
41
|
Europe
|
41
|
|
|
Andorra
|
42.50630
|
1.521800
|
2020-03-15
|
1
|
0
|
1
|
0
|
Europe
|
0
|
|
|
Austria
|
47.51620
|
14.550100
|
2020-03-15
|
860
|
1
|
6
|
853
|
Europe
|
853
|
|
|
Belarus
|
53.70980
|
27.953400
|
2020-03-15
|
27
|
0
|
3
|
24
|
Europe
|
24
|
|
|
Belgium
|
50.83330
|
4.469936
|
2020-03-15
|
886
|
4
|
1
|
881
|
Europe
|
881
|
|
|
Bosnia and Herzegovina
|
43.91590
|
17.679100
|
2020-03-15
|
24
|
0
|
0
|
24
|
Europe
|
24
|
|
|
Bulgaria
|
42.73390
|
25.485800
|
2020-03-15
|
51
|
2
|
0
|
49
|
Europe
|
49
|
|
|
Croatia
|
45.10000
|
15.200000
|
2020-03-15
|
49
|
0
|
1
|
48
|
Europe
|
48
|
|
|
Czechia
|
49.81750
|
15.473000
|
2020-03-15
|
253
|
0
|
0
|
253
|
Europe
|
253
|
|
Faroe Islands
|
Denmark
|
61.89260
|
-6.911800
|
2020-03-15
|
11
|
0
|
0
|
11
|
Europe
|
11
|
|
|
Denmark
|
56.26390
|
9.501800
|
2020-03-15
|
864
|
2
|
1
|
861
|
Europe
|
861
|
|
|
Estonia
|
58.59530
|
25.013600
|
2020-03-15
|
171
|
0
|
1
|
170
|
Europe
|
170
|
|
|
Finland
|
61.92411
|
25.748151
|
2020-03-15
|
244
|
0
|
10
|
234
|
Europe
|
234
|
|
|
France
|
46.22760
|
2.213700
|
2020-03-15
|
4499
|
91
|
12
|
4396
|
Europe
|
4396
|
|
|
Germany
|
51.16569
|
10.451526
|
2020-03-15
|
5795
|
11
|
46
|
5738
|
Europe
|
5738
|
|
|
Greece
|
39.07420
|
21.824300
|
2020-03-15
|
331
|
4
|
8
|
319
|
Europe
|
319
|
|
|
Holy See
|
41.90290
|
12.453400
|
2020-03-15
|
1
|
0
|
0
|
1
|
Europe
|
1
|
|
|
Hungary
|
47.16250
|
19.503300
|
2020-03-15
|
32
|
1
|
1
|
30
|
Europe
|
30
|
|
|
Iceland
|
64.96310
|
-19.020800
|
2020-03-15
|
171
|
5
|
8
|
158
|
Europe
|
158
|
|
|
Ireland
|
53.14240
|
-7.692100
|
2020-03-15
|
129
|
2
|
0
|
127
|
Europe
|
127
|
|
|
Italy
|
41.87194
|
12.567380
|
2020-03-15
|
24747
|
1809
|
2335
|
20603
|
Europe
|
20603
|
|
|
Latvia
|
56.87960
|
24.603200
|
2020-03-15
|
30
|
0
|
1
|
29
|
Europe
|
29
|
|
|
Liechtenstein
|
47.14000
|
9.550000
|
2020-03-15
|
4
|
0
|
1
|
3
|
Europe
|
3
|
|
|
Lithuania
|
55.16940
|
23.881300
|
2020-03-15
|
12
|
0
|
1
|
11
|
Europe
|
11
|
|
|
Luxembourg
|
49.81530
|
6.129600
|
2020-03-15
|
59
|
1
|
0
|
58
|
Europe
|
58
|
|
|
Moldova
|
47.41160
|
28.369900
|
2020-03-15
|
23
|
0
|
0
|
23
|
Europe
|
23
|
|
|
Monaco
|
43.73330
|
7.416700
|
2020-03-15
|
2
|
0
|
0
|
2
|
Europe
|
2
|
|
|
Montenegro
|
42.70868
|
19.374390
|
2020-03-15
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
|
Netherlands
|
52.13260
|
5.291300
|
2020-03-15
|
1135
|
20
|
0
|
1115
|
Europe
|
1115
|
|
|
North Macedonia
|
41.60860
|
21.745300
|
2020-03-15
|
14
|
0
|
1
|
13
|
Europe
|
13
|
|
|
Norway
|
60.47200
|
8.468900
|
2020-03-15
|
1221
|
3
|
1
|
1217
|
Europe
|
1217
|
|
|
Poland
|
51.91940
|
19.145100
|
2020-03-15
|
119
|
3
|
0
|
116
|
Europe
|
116
|
|
|
Portugal
|
39.39990
|
-8.224500
|
2020-03-15
|
245
|
0
|
2
|
243
|
Europe
|
243
|
|
|
Romania
|
45.94320
|
24.966800
|
2020-03-15
|
131
|
0
|
9
|
122
|
Europe
|
122
|
|
|
San Marino
|
43.94240
|
12.457800
|
2020-03-15
|
101
|
5
|
4
|
92
|
Europe
|
92
|
|
|
Serbia
|
44.01650
|
21.005900
|
2020-03-15
|
48
|
0
|
0
|
48
|
Europe
|
48
|
|
|
Slovakia
|
48.66900
|
19.699000
|
2020-03-15
|
54
|
0
|
0
|
54
|
Europe
|
54
|
|
|
Slovenia
|
46.15120
|
14.995500
|
2020-03-15
|
219
|
1
|
0
|
218
|
Europe
|
218
|
|
|
Spain
|
40.46367
|
-3.749220
|
2020-03-15
|
7798
|
289
|
517
|
6992
|
Europe
|
6992
|
|
|
Sweden
|
60.12816
|
18.643501
|
2020-03-15
|
1022
|
3
|
0
|
1019
|
Europe
|
1019
|
|
|
Switzerland
|
46.81820
|
8.227500
|
2020-03-15
|
2200
|
14
|
4
|
2182
|
Europe
|
2182
|
|
Channel Islands
|
United Kingdom
|
49.37230
|
-2.364400
|
2020-03-15
|
3
|
0
|
0
|
3
|
Europe
|
3
|
|
Isle of Man
|
United Kingdom
|
54.23610
|
-4.548100
|
2020-03-15
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
|
United Kingdom
|
55.37810
|
-3.436000
|
2020-03-15
|
3072
|
43
|
18
|
3011
|
Europe
|
3011
|
|
|
Kosovo
|
42.60264
|
20.902977
|
2020-03-15
|
0
|
0
|
0
|
0
|
Europe
|
0
|
Analisis Amercia Latina
datos_aml = datos %>%
filter(Latitud < 27 , between(Longitud, -121, -31))
table (datos_aml$Pais_Region) %>%
as.data.frame() %>%
filter(Freq > 0) %>%
kable() %>%
kable_styling()
|
Var1
|
Freq
|
|
Antigua and Barbuda
|
188
|
|
Argentina
|
188
|
|
Bahamas
|
188
|
|
Barbados
|
188
|
|
Belize
|
188
|
|
Bolivia
|
188
|
|
Brazil
|
188
|
|
Chile
|
188
|
|
Colombia
|
188
|
|
Costa Rica
|
188
|
|
Cuba
|
188
|
|
Dominica
|
188
|
|
Dominican Republic
|
188
|
|
Ecuador
|
188
|
|
El Salvador
|
188
|
|
France
|
940
|
|
Grenada
|
188
|
|
Guatemala
|
188
|
|
Guyana
|
188
|
|
Haiti
|
188
|
|
Honduras
|
188
|
|
Jamaica
|
188
|
|
Mexico
|
188
|
|
Netherlands
|
564
|
|
Nicaragua
|
188
|
|
Panama
|
188
|
|
Paraguay
|
188
|
|
Peru
|
188
|
|
Saint Kitts and Nevis
|
188
|
|
Saint Lucia
|
188
|
|
Saint Vincent and the Grenadines
|
188
|
|
Suriname
|
188
|
|
Trinidad and Tobago
|
188
|
|
United Kingdom
|
1128
|
|
Uruguay
|
188
|
|
Venezuela
|
188
|
datos_aml %>%
filter(Fecha == ymd("2020-03-3")) %>%
kable() %>%
kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
Casos_Enfermos
|
|
|
Antigua and Barbuda
|
17.060800
|
-61.79640
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Argentina
|
-38.416100
|
-63.61670
|
2020-03-03
|
1
|
0
|
0
|
1
|
Americas
|
1
|
|
|
Bahamas
|
25.025885
|
-78.03589
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Barbados
|
13.193900
|
-59.54320
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Bolivia
|
-16.290200
|
-63.58870
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Brazil
|
-14.235000
|
-51.92530
|
2020-03-03
|
2
|
0
|
0
|
2
|
Americas
|
2
|
|
|
Chile
|
-35.675100
|
-71.54300
|
2020-03-03
|
10
|
0
|
0
|
10
|
Americas
|
10
|
|
|
Colombia
|
4.570900
|
-74.29730
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Costa Rica
|
9.748900
|
-83.75340
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Cuba
|
21.521757
|
-77.78117
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Dominican Republic
|
18.735700
|
-70.16270
|
2020-03-03
|
1
|
0
|
0
|
1
|
Americas
|
1
|
|
|
Ecuador
|
-1.831200
|
-78.18340
|
2020-03-03
|
7
|
0
|
0
|
7
|
Americas
|
7
|
|
|
El Salvador
|
13.794200
|
-88.89650
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
French Guiana
|
France
|
3.933900
|
-53.12580
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Guadeloupe
|
France
|
16.265000
|
-61.55100
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Saint Barthelemy
|
France
|
17.900000
|
-62.83330
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
St Martin
|
France
|
18.070800
|
-63.05010
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Martinique
|
France
|
14.641500
|
-61.02420
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
|
Guatemala
|
15.783500
|
-90.23080
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Guyana
|
4.860416
|
-58.93018
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Haiti
|
18.971200
|
-72.28520
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Honduras
|
15.200000
|
-86.24190
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Jamaica
|
18.109600
|
-77.29750
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Mexico
|
23.634500
|
-102.55280
|
2020-03-03
|
5
|
0
|
1
|
4
|
Americas
|
4
|
|
Aruba
|
Netherlands
|
12.521100
|
-69.96830
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Curacao
|
Netherlands
|
12.169600
|
-68.99000
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Sint Maarten
|
Netherlands
|
18.042500
|
-63.05480
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
|
Nicaragua
|
12.865416
|
-85.20723
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Panama
|
8.538000
|
-80.78210
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Paraguay
|
-23.442500
|
-58.44380
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Peru
|
-9.190000
|
-75.01520
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Saint Lucia
|
13.909400
|
-60.97890
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Saint Vincent and the Grenadines
|
12.984300
|
-61.28720
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Suriname
|
3.919300
|
-56.02780
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Trinidad and Tobago
|
10.691800
|
-61.22250
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
Cayman Islands
|
United Kingdom
|
19.313300
|
-81.25460
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Montserrat
|
United Kingdom
|
16.742498
|
-62.18737
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
|
Uruguay
|
-32.522800
|
-55.76580
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Venezuela
|
6.423800
|
-66.58970
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Dominica
|
15.415000
|
-61.37100
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Grenada
|
12.116500
|
-61.67900
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Belize
|
17.189900
|
-88.49760
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
|
Saint Kitts and Nevis
|
17.357822
|
-62.78300
|
2020-03-03
|
0
|
0
|
0
|
0
|
Americas
|
0
|
|
Anguilla
|
United Kingdom
|
18.220600
|
-63.06860
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
British Virgin Islands
|
United Kingdom
|
18.420700
|
-64.64000
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Turks and Caicos Islands
|
United Kingdom
|
21.694000
|
-71.79790
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
|
Falkland Islands (Malvinas)
|
United Kingdom
|
-51.796300
|
-59.52360
|
2020-03-03
|
0
|
0
|
0
|
0
|
Europe
|
0
|
Mapas
world<-ne_countries(scale = "large", returnclass = "sf")
ggplot(data = world) +
geom_sf(color = "red", fill = "green") +
xlab("Logitud") + ylab("Latitud")+
ggtitle("Mapa del mundo", subtitle = "COVID 19")

world<-ne_countries(scale = "large", returnclass = "sf")
world2<-ne_countries(scale = "medium", returnclass = "sf")
#datos$Pais_Region = factor(datos$Pais_Region, levels = c(levels(datos$Pais_Region), "United States of America"))
#datos[datos$Pais_Region=="United States",]$Pais_Region ="United States of America"
world %>%
inner_join(datos, by = c("name"= "Pais_Region")) %>%
filter(Fecha == dmy("10-06-2020")) %>%
ggplot() +
geom_sf(color = "black", aes(fill = Casos_Confirmados)) +
scale_fill_viridis_c(option = "plasma", trans ="sqrt")+
xlab("Logitud") + ylab("Latitud")+
ggtitle("Mapa del mundo", subtitle = "COVID 19")

datos %>%
filter(Fecha == dmy("20-4-2020")) %>%
ggplot(aes(Longitud, Latitud))+
geom_point(aes(size=log(Casos_Confirmados+1), colour=log(Casos_Muertos+1)))+
coord_fixed()+
theme(legend.position = "bottom") -> g
ggplotly(g)
thr = 50000
datos %>%
filter(Fecha == ymd("2020-07-27"), Casos_Confirmados > thr) %>%
mutate(Prop_Muertos = Casos_Muertos / Casos_Confirmados, Ranking = dense_rank(desc(Prop_Muertos))) %>%
arrange(Ranking) %>%
head(20) %>%
kable() %>%
kable_styling()
|
Provincia_Estado
|
Pais_Region
|
Latitud
|
Longitud
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
WHO_Region
|
Casos_Enfermos
|
Prop_Muertos
|
Ranking
|
|
|
United Kingdom
|
55.37810
|
-3.436000
|
2020-07-27
|
300111
|
45759
|
0
|
254352
|
Europe
|
254352
|
0.1524736
|
1
|
|
|
Belgium
|
50.83330
|
4.469936
|
2020-07-27
|
66428
|
9822
|
17452
|
39154
|
Europe
|
39154
|
0.1478593
|
2
|
|
|
France
|
46.22760
|
2.213700
|
2020-07-27
|
208665
|
30096
|
71497
|
107072
|
Europe
|
107072
|
0.1442312
|
3
|
|
|
Italy
|
41.87194
|
12.567380
|
2020-07-27
|
246286
|
35112
|
198593
|
12581
|
Europe
|
12581
|
0.1425660
|
4
|
|
|
Netherlands
|
52.13260
|
5.291300
|
2020-07-27
|
53151
|
6141
|
0
|
47010
|
Europe
|
47010
|
0.1155387
|
5
|
|
|
Mexico
|
23.63450
|
-102.552800
|
2020-07-27
|
395489
|
44022
|
303810
|
47657
|
Americas
|
47657
|
0.1113103
|
6
|
|
|
Spain
|
40.46367
|
-3.749220
|
2020-07-27
|
272421
|
28432
|
150376
|
93613
|
Europe
|
93613
|
0.1043679
|
7
|
|
Quebec
|
Canada
|
52.93990
|
-73.549100
|
2020-07-27
|
58728
|
5667
|
0
|
53061
|
Americas
|
53061
|
0.0964957
|
8
|
|
|
Sweden
|
60.12816
|
18.643501
|
2020-07-27
|
79395
|
5700
|
0
|
73695
|
Europe
|
73695
|
0.0717929
|
9
|
|
|
Ecuador
|
-1.83120
|
-78.183400
|
2020-07-27
|
81161
|
5532
|
34896
|
40733
|
Americas
|
40733
|
0.0681608
|
10
|
|
Hubei
|
China
|
30.97560
|
112.270700
|
2020-07-27
|
68135
|
4512
|
63623
|
0
|
Western Pacific
|
0
|
0.0662215
|
11
|
|
|
Iran
|
32.42791
|
53.688046
|
2020-07-27
|
293606
|
15912
|
255144
|
22550
|
Eastern Mediterranean
|
22550
|
0.0541951
|
12
|
|
|
Egypt
|
26.82055
|
30.802498
|
2020-07-27
|
92482
|
4652
|
34838
|
52992
|
Eastern Mediterranean
|
52992
|
0.0503017
|
13
|
|
|
Indonesia
|
-0.78930
|
113.921300
|
2020-07-27
|
100303
|
4838
|
58173
|
37292
|
South-East Asia
|
37292
|
0.0482339
|
14
|
|
|
Peru
|
-9.19000
|
-75.015200
|
2020-07-27
|
389717
|
18418
|
272547
|
98752
|
Americas
|
98752
|
0.0472599
|
15
|
|
|
Germany
|
51.16569
|
10.451526
|
2020-07-27
|
207112
|
9125
|
190314
|
7673
|
Europe
|
7673
|
0.0440583
|
16
|
|
|
Iraq
|
33.22319
|
43.679291
|
2020-07-27
|
112585
|
4458
|
77144
|
30983
|
Eastern Mediterranean
|
30983
|
0.0395967
|
17
|
|
|
Bolivia
|
-16.29020
|
-63.588700
|
2020-07-27
|
71181
|
2647
|
21478
|
47056
|
Americas
|
47056
|
0.0371869
|
18
|
|
|
Brazil
|
-14.23500
|
-51.925300
|
2020-07-27
|
2442375
|
87618
|
1846641
|
508116
|
Americas
|
508116
|
0.0358741
|
19
|
|
|
US
|
40.00000
|
-100.000000
|
2020-07-27
|
4290259
|
148011
|
1325804
|
2816444
|
Americas
|
2816444
|
0.0344993
|
20
|
datos$lat_class = cut(datos$Latitud, breaks = nclass.scott(datos$Latitud))
datos$long_class = cut(datos$Longitud, breaks = nclass.Sturges(datos$Longitud))
tt = table(datos$lat_class, datos$long_class)
tt = tt[nrow(tt):1,]
mosaicplot(t(tt), shade = TRUE)

Series Temporales
datos_por_fecha<-aggregate(
cbind(Casos_Confirmados, Casos_Muertos, Casos_Recuperados) ~ Fecha,
data = datos,
FUN = sum)
head(datos_por_fecha)
## Fecha Casos_Confirmados Casos_Muertos Casos_Recuperados
## 1 2020-01-22 555 17 28
## 2 2020-01-23 654 18 30
## 3 2020-01-24 941 26 36
## 4 2020-01-25 1434 42 39
## 5 2020-01-26 2118 56 52
## 6 2020-01-27 2927 82 61
tail(datos_por_fecha)
## Fecha Casos_Confirmados Casos_Muertos Casos_Recuperados
## 183 2020-07-22 15227725 623540 8541255
## 184 2020-07-23 15510481 633506 8710969
## 185 2020-07-24 15791645 639650 8939705
## 186 2020-07-25 16047190 644517 9158743
## 187 2020-07-26 16251796 648621 9293464
## 188 2020-07-27 16480485 654036 9468087
barplot(Casos_Confirmados ~ Fecha, data=datos_por_fecha)

plot(Casos_Confirmados ~ Fecha, data= datos_por_fecha, col ="blue", type ="l",
main="Casos Documentados", xlab="Fecha", ylab="Numeros de Personas", log="y" )
lines(Casos_Muertos ~ Fecha, data=datos_por_fecha, col="red")
lines(Casos_Recuperados ~ Fecha, data=datos_por_fecha, col="green" )
legend("topleft", c("Confirmados", "Muertos", "Recuperados"), col=c("blue", "red", "green"), pch = 1, lwd = 2)

datos_por_fecha_ts<-xts(x=datos_por_fecha[,2:4], order.by = datos_por_fecha$Fecha)
dygraph(datos_por_fecha_ts)%>%
dyOptions(labelsKMB = TRUE,
fillGraph = TRUE, fillAlpha = 0,05,
drawGrid = FALSE) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical")%>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2,hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 2)
datos_chile<-datos %>%
filter(Pais_Region== "Chile") %>%
select(Fecha, starts_with("Casos_"))
plot(x=datos_chile$Fecha, y=datos_chile$Casos_Confirmados, main="Casos Chile", type="s", col="blue", lwd=2)

barplot(as.matrix(t(datos_chile[,3:5])),
col = c("red", "green", "yellow"),
main="Casos Chile",
xlab="Fecha", ylab = "Personas")
legend("topleft", c("Muertos", "Recuperados", "Enfermos"),
col=c("red", "green", "yellow"), lwd=2, pch = 1)
# Nuevos Casos
datos_chile %<>%
mutate(Nuevos_Casos_Confirmados =Casos_Confirmados- lag(Casos_Confirmados, n=1),
Nuevos_Casos_Muerto = Casos_Muertos - lag(Casos_Muertos, n=1),
Nuevos_Casos_Recuperados = Casos_Recuperados - lag(Casos_Recuperados, n=1))
datos_chile %>% kable() %>% kable_styling()
|
Fecha
|
Casos_Confirmados
|
Casos_Muertos
|
Casos_Recuperados
|
Casos_Activos
|
Casos_Enfermos
|
Nuevos_Casos_Confirmados
|
Nuevos_Casos_Muerto
|
Nuevos_Casos_Recuperados
|
|
2020-01-22
|
0
|
0
|
0
|
0
|
0
|
NA
|
NA
|
NA
|
|
2020-01-23
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-24
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-25
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-26
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-27
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-28
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-29
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-30
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-01-31
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-01
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-02
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-03
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-04
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-05
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-06
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-07
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-08
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-09
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-10
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-11
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-12
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-13
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-14
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-15
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-16
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-17
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-18
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-19
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-20
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-21
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-22
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
2020-02-23
|
2
|
0
|
0
|
2
|
2
|
2
|
0
|
0
|
|
2020-02-24
|
2
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
|
2020-02-25
|
2
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
|
2020-02-26
|
2
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
|
2020-02-27
|
2
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
|
2020-02-28
|
2
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
|
2020-02-29
|
2
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
|
2020-03-01
|
9
|
0
|
0
|
9
|
9
|
7
|
0
|
0
|
|
2020-03-02
|
9
|
0
|
0
|
9
|
9
|
0
|
0
|
0
|
|
2020-03-03
|
10
|
0
|
0
|
10
|
10
|
1
|
0
|
0
|
|
2020-03-04
|
10
|
0
|
0
|
10
|
10
|
0
|
0
|
0
|
|
2020-03-05
|
13
|
0
|
0
|
13
|
13
|
3
|
0
|
0
|
|
2020-03-06
|
13
|
0
|
0
|
13
|
13
|
0
|
0
|
0
|
|
2020-03-07
|
13
|
0
|
0
|
13
|
13
|
0
|
0
|
0
|
|
2020-03-08
|
20
|
0
|
0
|
20
|
20
|
7
|
0
|
0
|
|
2020-03-09
|
20
|
0
|
0
|
20
|
20
|
0
|
0
|
0
|
|
2020-03-10
|
25
|
0
|
0
|
25
|
25
|
5
|
0
|
0
|
|
2020-03-11
|
35
|
0
|
0
|
35
|
35
|
10
|
0
|
0
|
|
2020-03-12
|
35
|
0
|
0
|
35
|
35
|
0
|
0
|
0
|
|
2020-03-13
|
55
|
0
|
0
|
55
|
55
|
20
|
0
|
0
|
|
2020-03-14
|
88
|
0
|
0
|
88
|
88
|
33
|
0
|
0
|
|
2020-03-15
|
101
|
0
|
0
|
101
|
101
|
13
|
0
|
0
|
|
2020-03-16
|
182
|
0
|
0
|
182
|
182
|
81
|
0
|
0
|
|
2020-03-17
|
228
|
0
|
0
|
228
|
228
|
46
|
0
|
0
|
|
2020-03-18
|
265
|
0
|
0
|
265
|
265
|
37
|
0
|
0
|
|
2020-03-19
|
265
|
0
|
0
|
265
|
265
|
0
|
0
|
0
|
|
2020-03-20
|
461
|
0
|
6
|
455
|
455
|
196
|
0
|
6
|
|
2020-03-21
|
592
|
0
|
6
|
586
|
586
|
131
|
0
|
0
|
|
2020-03-22
|
687
|
1
|
8
|
678
|
678
|
95
|
1
|
2
|
|
2020-03-23
|
801
|
2
|
8
|
791
|
791
|
114
|
1
|
0
|
|
2020-03-24
|
977
|
2
|
17
|
958
|
958
|
176
|
0
|
9
|
|
2020-03-25
|
1197
|
3
|
22
|
1172
|
1172
|
220
|
1
|
5
|
|
2020-03-26
|
1361
|
4
|
22
|
1335
|
1335
|
164
|
1
|
0
|
|
2020-03-27
|
1665
|
5
|
43
|
1617
|
1617
|
304
|
1
|
21
|
|
2020-03-28
|
2015
|
6
|
61
|
1948
|
1948
|
350
|
1
|
18
|
|
2020-03-29
|
2245
|
7
|
75
|
2163
|
2163
|
230
|
1
|
14
|
|
2020-03-30
|
2555
|
8
|
156
|
2391
|
2391
|
310
|
1
|
81
|
|
2020-03-31
|
2844
|
12
|
156
|
2676
|
2676
|
289
|
4
|
0
|
|
2020-04-01
|
3137
|
16
|
234
|
2887
|
2887
|
293
|
4
|
78
|
|
2020-04-02
|
3510
|
18
|
335
|
3157
|
3157
|
373
|
2
|
101
|
|
2020-04-03
|
3843
|
22
|
427
|
3394
|
3394
|
333
|
4
|
92
|
|
2020-04-04
|
4355
|
27
|
528
|
3800
|
3800
|
512
|
5
|
101
|
|
2020-04-05
|
4665
|
34
|
618
|
4013
|
4013
|
310
|
7
|
90
|
|
2020-04-06
|
5009
|
37
|
728
|
4244
|
4244
|
344
|
3
|
110
|
|
2020-04-07
|
5310
|
43
|
898
|
4369
|
4369
|
301
|
6
|
170
|
|
2020-04-08
|
5740
|
48
|
1115
|
4577
|
4577
|
430
|
5
|
217
|
|
2020-04-09
|
6166
|
57
|
1274
|
4835
|
4835
|
426
|
9
|
159
|
|
2020-04-10
|
6695
|
65
|
1571
|
5059
|
5059
|
529
|
8
|
297
|
|
2020-04-11
|
7366
|
73
|
1864
|
5429
|
5429
|
671
|
8
|
293
|
|
2020-04-12
|
7652
|
80
|
2059
|
5513
|
5513
|
286
|
7
|
195
|
|
2020-04-13
|
7964
|
82
|
2367
|
5515
|
5515
|
312
|
2
|
308
|
|
2020-04-14
|
8356
|
92
|
2646
|
5618
|
5618
|
392
|
10
|
279
|
|
2020-04-15
|
8712
|
94
|
2937
|
5681
|
5681
|
356
|
2
|
291
|
|
2020-04-16
|
9246
|
105
|
3299
|
5842
|
5842
|
534
|
11
|
362
|
|
2020-04-17
|
9691
|
116
|
3621
|
5954
|
5954
|
445
|
11
|
322
|
|
2020-04-18
|
10598
|
126
|
4035
|
6437
|
6437
|
907
|
10
|
414
|
|
2020-04-19
|
10956
|
133
|
4338
|
6485
|
6485
|
358
|
7
|
303
|
|
2020-04-20
|
11375
|
139
|
4676
|
6560
|
6560
|
419
|
6
|
338
|
|
2020-04-21
|
11700
|
147
|
4969
|
6584
|
6584
|
325
|
8
|
293
|
|
2020-04-22
|
12164
|
160
|
5386
|
6618
|
6618
|
464
|
13
|
417
|
|
2020-04-23
|
12680
|
168
|
5804
|
6708
|
6708
|
516
|
8
|
418
|
|
2020-04-24
|
13174
|
174
|
6327
|
6673
|
6673
|
494
|
6
|
523
|
|
2020-04-25
|
14537
|
181
|
6746
|
7610
|
7610
|
1363
|
7
|
419
|
|
2020-04-26
|
15010
|
189
|
7024
|
7797
|
7797
|
473
|
8
|
278
|
|
2020-04-27
|
15492
|
198
|
7327
|
7967
|
7967
|
482
|
9
|
303
|
|
2020-04-28
|
16044
|
207
|
7710
|
8127
|
8127
|
552
|
9
|
383
|
|
2020-04-29
|
16564
|
216
|
8057
|
8291
|
8291
|
520
|
9
|
347
|
|
2020-04-30
|
17702
|
227
|
8580
|
8895
|
8895
|
1138
|
11
|
523
|
|
2020-05-01
|
18687
|
234
|
9018
|
9435
|
9435
|
985
|
7
|
438
|
|
2020-05-02
|
21213
|
247
|
9572
|
11394
|
11394
|
2526
|
13
|
554
|
|
2020-05-03
|
22441
|
260
|
10041
|
12140
|
12140
|
1228
|
13
|
469
|
|
2020-05-04
|
23421
|
270
|
10415
|
12736
|
12736
|
980
|
10
|
374
|
|
2020-05-05
|
24794
|
275
|
10710
|
13809
|
13809
|
1373
|
5
|
295
|
|
2020-05-06
|
25826
|
281
|
11189
|
14356
|
14356
|
1032
|
6
|
479
|
|
2020-05-07
|
27359
|
285
|
11664
|
15410
|
15410
|
1533
|
4
|
475
|
|
2020-05-08
|
28750
|
294
|
12160
|
16296
|
16296
|
1391
|
9
|
496
|
|
2020-05-09
|
32208
|
304
|
12667
|
19237
|
19237
|
3458
|
10
|
507
|
|
2020-05-10
|
33855
|
312
|
13112
|
20431
|
20431
|
1647
|
8
|
445
|
|
2020-05-11
|
35052
|
323
|
13605
|
21124
|
21124
|
1197
|
11
|
493
|
|
2020-05-12
|
36710
|
335
|
14125
|
22250
|
22250
|
1658
|
12
|
520
|
|
2020-05-13
|
39370
|
346
|
14865
|
24159
|
24159
|
2660
|
11
|
740
|
|
2020-05-14
|
42029
|
368
|
15655
|
26006
|
26006
|
2659
|
22
|
790
|
|
2020-05-15
|
44531
|
394
|
16614
|
27523
|
27523
|
2502
|
26
|
959
|
|
2020-05-16
|
50016
|
421
|
18014
|
31581
|
31581
|
5485
|
27
|
1400
|
|
2020-05-17
|
52369
|
450
|
19213
|
32706
|
32706
|
2353
|
29
|
1199
|
|
2020-05-18
|
54647
|
478
|
20165
|
34004
|
34004
|
2278
|
28
|
952
|
|
2020-05-19
|
58167
|
509
|
21507
|
36151
|
36151
|
3520
|
31
|
1342
|
|
2020-05-20
|
62205
|
544
|
22504
|
39157
|
39157
|
4038
|
35
|
997
|
|
2020-05-21
|
66169
|
589
|
23992
|
41588
|
41588
|
3964
|
45
|
1488
|
|
2020-05-22
|
70445
|
630
|
25342
|
44473
|
44473
|
4276
|
41
|
1350
|
|
2020-05-23
|
80287
|
673
|
26546
|
53068
|
53068
|
9842
|
43
|
1204
|
|
2020-05-24
|
83996
|
718
|
28148
|
55130
|
55130
|
3709
|
45
|
1602
|
|
2020-05-25
|
88891
|
761
|
29302
|
58828
|
58828
|
4895
|
43
|
1154
|
|
2020-05-26
|
92855
|
806
|
30915
|
61134
|
61134
|
3964
|
45
|
1613
|
|
2020-05-27
|
97183
|
841
|
33540
|
62802
|
62802
|
4328
|
35
|
2625
|
|
2020-05-28
|
101837
|
890
|
36115
|
64832
|
64832
|
4654
|
49
|
2575
|
|
2020-05-29
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105532
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944
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38598
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65990
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65990
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3695
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54
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2483
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2020-05-30
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118720
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997
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40431
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77292
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77292
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13188
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53
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1833
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2020-05-31
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123550
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1054
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42727
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79769
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79769
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4830
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57
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2296
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2020-06-01
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129020
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1113
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44946
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82961
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82961
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5470
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59
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2219
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2020-06-02
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132548
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1188
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86173
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45187
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45187
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3528
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75
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41227
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2020-06-03
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137490
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1275
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90748
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45467
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45467
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4942
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87
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4575
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2020-06-04
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142154
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1356
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95631
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45167
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45167
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4664
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81
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4883
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2020-06-05
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146361
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1448
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99358
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45555
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45555
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4207
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92
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3727
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2020-06-06
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160351
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1541
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103817
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54993
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54993
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13990
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93
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4459
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2020-06-07
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166756
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1637
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108150
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56969
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56969
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6405
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96
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4333
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2020-06-08
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171452
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2264
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112248
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56940
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56940
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4696
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627
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4098
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2020-06-09
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175365
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2283
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117361
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55721
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55721
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3913
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19
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5113
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2020-06-10
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181062
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2475
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121780
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56807
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56807
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5697
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192
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4419
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2020-06-11
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186698
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2648
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126444
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57606
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57606
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5636
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173
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4664
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2020-06-12
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193452
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2870
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131358
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59224
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59224
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6754
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222
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4914
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2020-06-13
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201634
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3101
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137296
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61237
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61237
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8182
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231
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5938
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2020-06-14
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208572
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3323
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143704
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61545
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61545
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6938
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222
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6408
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2020-06-15
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213715
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3362
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148792
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61561
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61561
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5143
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39
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5088
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2020-06-16
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218728
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3383
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156232
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59113
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59113
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5013
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21
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7440
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2020-06-17
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220628
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3615
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181931
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35082
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35082
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1900
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232
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25699
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2020-06-18
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225103
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3841
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186441
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34821
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34821
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4475
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226
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4510
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2020-06-19
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231393
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4093
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191491
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35809
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35809
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6290
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252
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5050
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2020-06-20
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236748
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4295
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196609
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35844
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35844
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5355
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202
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5118
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2020-06-21
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242355
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4479
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200569
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37307
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37307
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5607
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184
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3960
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|
2020-06-22
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246963
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4502
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205397
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37064
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37064
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4608
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23
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4828
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2020-06-23
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250767
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4505
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210570
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35692
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35692
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3804
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3
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5173
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2020-06-24
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254416
|
4731
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215093
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34592
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34592
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3649
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226
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4523
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|
2020-06-25
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259064
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4903
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219327
|
34834
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34834
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4648
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172
|
4234
|
|
2020-06-26
|
263360
|
5068
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223431
|
34861
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34861
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4296
|
165
|
4104
|
|
2020-06-27
|
267766
|
5347
|
228055
|
34364
|
34364
|
4406
|
279
|
4624
|
|
2020-06-28
|
271982
|
5509
|
232210
|
34263
|
34263
|
4216
|
162
|
4155
|
|
2020-06-29
|
275999
|
5575
|
236154
|
34270
|
34270
|
4017
|
66
|
3944
|
|
2020-06-30
|
279393
|
5688
|
241229
|
32476
|
32476
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3394
|
113
|
5075
|
|
2020-07-01
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282043
|
5753
|
245443
|
30847
|
30847
|
2650
|
65
|
4214
|
|
2020-07-02
|
284541
|
5920
|
249247
|
29374
|
29374
|
2498
|
167
|
3804
|
|
2020-07-03
|
288089
|
6051
|
253343
|
28695
|
28695
|
3548
|
131
|
4096
|
|
2020-07-04
|
291847
|
6192
|
257451
|
28204
|
28204
|
3758
|
141
|
4108
|
|
2020-07-05
|
295532
|
6308
|
261039
|
28185
|
28185
|
3685
|
116
|
3588
|
|
2020-07-06
|
298557
|
6384
|
264378
|
27795
|
27795
|
3025
|
76
|
3339
|
|
2020-07-07
|
301019
|
6434
|
268251
|
26334
|
26334
|
2462
|
50
|
3873
|
|
2020-07-08
|
303083
|
6573
|
271741
|
24769
|
24769
|
2064
|
139
|
3490
|
|
2020-07-09
|
306216
|
6682
|
274922
|
24612
|
24612
|
3133
|
109
|
3181
|
|
2020-07-10
|
309274
|
6781
|
278053
|
24440
|
24440
|
3058
|
99
|
3131
|
|
2020-07-11
|
312029
|
6881
|
281114
|
24034
|
24034
|
2755
|
100
|
3061
|
|
2020-07-12
|
315041
|
6979
|
283902
|
24160
|
24160
|
3012
|
98
|
2788
|
|
2020-07-13
|
317657
|
7024
|
286556
|
24077
|
24077
|
2616
|
45
|
2654
|
|
2020-07-14
|
319493
|
7069
|
289220
|
23204
|
23204
|
1836
|
45
|
2664
|
|
2020-07-15
|
321205
|
7186
|
292085
|
21934
|
21934
|
1712
|
117
|
2865
|
|
2020-07-16
|
323698
|
7290
|
295301
|
21107
|
21107
|
2493
|
104
|
3216
|
|
2020-07-17
|
326439
|
8347
|
296814
|
21278
|
21278
|
2741
|
1057
|
1513
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|
2020-07-18
|
328846
|
8445
|
299449
|
20952
|
20952
|
2407
|
98
|
2635
|
|
2020-07-19
|
330930
|
8503
|
301794
|
20633
|
20633
|
2084
|
58
|
2345
|
|
2020-07-20
|
333029
|
8633
|
303992
|
20404
|
20404
|
2099
|
130
|
2198
|
|
2020-07-21
|
334683
|
8677
|
306816
|
19190
|
19190
|
1654
|
44
|
2824
|
|
2020-07-22
|
336402
|
8722
|
309241
|
18439
|
18439
|
1719
|
45
|
2425
|
|
2020-07-23
|
338759
|
8838
|
311431
|
18490
|
18490
|
2357
|
116
|
2190
|
|
2020-07-24
|
341304
|
8914
|
313696
|
18694
|
18694
|
2545
|
76
|
2265
|
|
2020-07-25
|
343592
|
9020
|
316169
|
18403
|
18403
|
2288
|
106
|
2473
|
|
2020-07-26
|
345790
|
9112
|
318095
|
18583
|
18583
|
2198
|
92
|
1926
|
|
2020-07-27
|
347923
|
9187
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319954
|
18782
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18782
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2133
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75
|
1859
|
plot(Nuevos_Casos_Confirmados ~ Fecha, data = datos_chile,
type = "l", col ="blue",
xlab = "Fecha", ylab = "Nuevos casos",
main = "Nuevos registros en Chile")
lines(Nuevos_Casos_Muerto ~ Fecha, data = datos_chile,
type = "l", col = "red")
lines(Nuevos_Casos_Recuperados ~ Fecha, data = datos_chile,
type = "l", col = "green")
legend("topleft", c("Confirmados", "Muertos", "Recuperados"),
col = c("blue", "red", "green"),
lwd = 2, pch = 1)

Cohortes
primer_contagio <- datos %>%
group_by(Pais_Region) %>%
filter(Casos_Confirmados > 0) %>%
summarise(Primero = min(Fecha)-1)
## `summarise()` ungrouping output (override with `.groups` argument)
data_first <- datos %>%
inner_join(primer_contagio, by="Pais_Region") %>%
mutate(Dias_Desde_PC = as.numeric(Fecha - Primero))%>%
filter(Dias_Desde_PC >=0)%>%
group_by(Dias_Desde_PC, Pais_Region)%>%
summarise(Casos_Confirmados = sum(Casos_Confirmados),
Casos_Muertos = sum(Casos_Muertos),
Casos_Recuperados = sum(Casos_Recuperados))
## `summarise()` regrouping output by 'Dias_Desde_PC' (override with `.groups` argument)
data_first %>%
filter(Pais_Region %in% c("Chile", "Peru", "Argentina", "China", "US")) %>%
ggplot(aes(x= Dias_Desde_PC, y = Casos_Confirmados)) +
geom_line(aes(col =Pais_Region)) +
xlab("Dias desde primer contagio")+
ylab("Personas")+
ggtitle("Analisis por Cohorte - Casos Confirmados")+
theme(legend.position = "none") -> h
ggplotly(h)
data_first %>%
filter(Pais_Region %in% c("Chile", "Peru", "Argentina", "China", "US")) %>%
ggplot(aes(x= Dias_Desde_PC, y = Casos_Muertos)) +
geom_line(aes(col =Pais_Region)) +
xlab("Dias desde primer contagio")+
ylab("Personas")+
ggtitle("Analisis por Cohorte - Muertes")+
theme(legend.position = "none") -> g
ggplotly(g)
Modelos de Regresion
datos_chile$Dias <- as.numeric(datos_chile$Fecha - dmy("22/01/2020"))
Regresion Lineal
mod1<- lm(Casos_Confirmados ~ Dias, data = datos_chile)
summary(mod1)
##
## Call:
## lm(formula = Casos_Confirmados ~ Dias, data = datos_chile)
##
## Residuals:
## Min 1Q Median 3Q Max
## -87613 -55413 1431 57268 91927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -91926.66 8442.17 -10.89 <2e-16 ***
## Dias 1946.63 78.09 24.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 58110 on 186 degrees of freedom
## Multiple R-squared: 0.7696, Adjusted R-squared: 0.7684
## F-statistic: 621.4 on 1 and 186 DF, p-value: < 2.2e-16
plot(datos_chile$Dias, datos_chile$Casos_Confirmados)
abline(mod1, col ="red")

# Residuo es el Error y los valores ajustados es la prediccion
plot(mod1$residuals ~ mod1$fitted.values, xlab="Valores Ajustados", ylab = "Residuos")

residuos <-mod1$residuals
qqPlot(residuos, distribution = "norm", mean= mean(residuos), sd=sd(residuos))

## [1] 1 2
Regresion Exponencial
mod2<-lm(log(Casos_Confirmados) ~ Dias, data= datos_chile[datos_chile$Casos_Confirmados>0, ])
summary(mod2)
##
## Call:
## lm(formula = log(Casos_Confirmados) ~ Dias, data = datos_chile[datos_chile$Casos_Confirmados >
## 0, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6994 -0.9694 0.5904 1.0632 1.5451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.708172 0.294128 5.808 3.51e-08 ***
## Dias 0.070643 0.002484 28.436 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 154 degrees of freedom
## Multiple R-squared: 0.84, Adjusted R-squared: 0.839
## F-statistic: 808.6 on 1 and 154 DF, p-value: < 2.2e-16
plot(datos_chile$Dias, datos_chile$Casos_Confirmados)
lines(exp(mod2$coefficients[1])*exp(mod2$coefficients[2]*datos_chile$Dias), col="blue")

plot(mod2$residuals ~ mod2$fitted.values, xlab="Valores ajustados", ylab="Residuos")

residuos2 <-mod2$residuals
qqPlot(residuos2, distribution = "norm", mean = mean(residuos2), sd=sd(residuos2))

## 39 38
## 7 6
Regresion Potencial
mod3 <- lm(log(Casos_Confirmados) ~ log(Dias), data = datos_chile[datos_chile$Casos_Confirmados >0,] )
summary(mod3)
##
## Call:
## lm(formula = log(Casos_Confirmados) ~ log(Dias), data = datos_chile[datos_chile$Casos_Confirmados >
## 0, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9399 -0.6021 0.2460 0.3864 1.0969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -23.2705 0.5507 -42.25 <2e-16 ***
## log(Dias) 7.1211 0.1192 59.72 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7107 on 154 degrees of freedom
## Multiple R-squared: 0.9586, Adjusted R-squared: 0.9583
## F-statistic: 3567 on 1 and 154 DF, p-value: < 2.2e-16
plot(datos_chile$Dias, datos_chile$Casos_Confirmados)
lines(exp(mod3$coefficients[1])*datos_chile$Dias^mod3$coefficients[2], col="green")

plot(mod3$residuals ~ mod3$fitted.values, xlab="Valores Ajustados", ylab="Residuos")

residuos3<- mod3$residuals
qqPlot(residuos3, distribution = "norm", mean=mean(residuos3), sd=sd(residuos3))

## 39 38
## 7 6